REVIEW 1 cited by
Sequence to Sequence Learning for Event Prediction
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
This paper presents an approach to the task of predicting an event description from a preceding sentence in a text. Our approach explores sequence-to-sequence learning using a bidirectional multi-layer recurrent neural network. Our approach substantially outperforms previous work in terms of the BLEU score on two datasets derived from WikiHow and DeScript respectively. Since the BLEU score is not easy to interpret as a measure of event prediction, we complement our study with a second evaluation that exploits the rich linguistic annotation of gold paraphrase sets of events.
Forward citations
Cited by 1 Pith paper
-
TransSent: Towards Generation of Structured Sentences with Discourse Marker
TransSent generates a tail discourse from a head discourse and a discourse marker by treating the marker as a translation in embedding space, with new datasets and improved scores over baselines.
Discussion (0). Continue with ORCID to comment.